Airbnb User Trajectories

Follow Airbnb users from a US city over the course of a year as they interact with the platform for different lengths of time and from different devices.

SessionUser

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ALL

Searched
Messaged
Booked

The purpose of this visualization is to identify patterns in the behavior of users accessing the Airbnb platform. Airbnb open-sourced a one year chunk of user data from a US city for a visualization challange at databits.io. It was a very detailed dataset, so the challenge was to identify and display only a few key patterns - I think the temptation is usually to show too much.

One thing that immediately stood out was the lull in activity between the hours of 14 and 18. You would expect a drop in activity overnight, not in the middle of the afternoon, so it may be that the time stamps in the dataset were shifted. Also interesting that the top 5 users, who accessed the platform >100 times, never booked.

A few other observations (that would need deeper analysis for confirmation):

Users access Airbnb episodically, probably correlating with more frequent visits just before travelling.

Most users are on multiple devices with no apparent correlation with the time of day.

Access to the platform tapers over time - NumSessions ~ t^1/2 ?

Heavy use of Python and pandas for the initial exploration and d3.js for the final visualization.